Atlas Discovery
Predicting human response to drugs in clinical trials
About Atlas Discovery
Predicting human response to drugs in clinical trials
Public traction evidence
Each signal links to the public source used for attribution.
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Nine out of ten drugs fail in clinical trials. The reason isn't a shortage of data. It's that none of it connects. We started Atlas Disc...
Nine out of ten drugs fail in clinical trials. The reason isn't a shortage of data. It's that none of it connects. We started Atlas Discovery to fix that with foundation models of patient drug response. We're backed by @ycombinator, @pearvc, and more.
- LinkedIn
Founder says he started Atlas Discovery. Post says Atlas Discovery raised funding from Y Combinator.
Founder says he started Atlas Discovery. Post says Atlas Discovery raised funding from Y Combinator.
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We trained a foundation model of patient biology that predicts drug response from a single biopsy before treatment. As a case study, we tested on a phase 3 trial of ustekinumab in IBD and get a 0.76 AUROC, enough to run it with ~450 fewer patients at the same statistical power.
We trained a foundation model of patient biology that predicts drug response from a single biopsy before treatment. As a case study, we tested on a phase 3 trial of ustekinumab in IBD and get a 0.76 AUROC, enough to run it with ~450 fewer patients at the same statistical power.
- X
We’re building foundation models that predict patient response in clinical trials. Excited for the future at @atlasdiscovery0 with @ShaamilKarim1 and @Cgensbigler ! Quote Atlas Discovery @atlasdiscovery0 · Jun 23 Nine out of ten drugs fail in clinical trials. The reason isn't a shortage of data. It's that none of it connects. We started Atlas Discovery to fix that with foundation models of patient drug response. We're backed by @ycombinator, @pearvc, and more.
We’re building foundation models that predict patient response in clinical trials. Excited for the future at @atlasdiscovery0 with @ShaamilKarim1 and @Cgensbigler ! Quote Atlas Discovery @atlasdiscovery0 · Jun 23 Nine out of ten drugs fail in clinical trials. The reason isn't a...
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Applied to a phase 3 trial, our model predicted which IBD patients respond to ustekinumab at 0.76 AUROC — attending to the inflammation &...
Applied to a phase 3 trial, our model predicted which IBD patients respond to ustekinumab at 0.76 AUROC — attending to the inflammation & fibrosis biology you'd expect. Better prediction doesn't just cut costs, it could make previously infeasible trials possible.
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I've been eagerly waiting for this team to launch and so excited that the world gets to meet them. We met @ShaamilKarim1 almost a year ag…
I've been eagerly waiting for this team to launch and so excited that the world gets to meet them. We met @ShaamilKarim1 almost a year ago for the first time, and it was abundantly clear that he had a unique polymathic combination of machine learning and biology expertise,...
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We were curious whether the autoregressive next token prediction recipe that has worked for language can be applied to single-cell biolog...
We were curious whether the autoregressive next token prediction recipe that has worked for language can be applied to single-cell biology. We first tried to understand if a discrete representation is the right substrate for modelling in this domain? Tldr: Yes! We studied a...
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Excited to share our paper at @icmlconf genbio right now. We ablate the core design choices in latent perturbation prediction and then te...
Excited to share our paper at @icmlconf genbio right now. We ablate the core design choices in latent perturbation prediction and then tested the model on a real-world out-of-distribution target discovery task from Pfizer, achieving SOTA results. Read more from @splicewiring!